DataOps vs. MLOps: Streamline your data operations

Key requirements to deploy DataOps

In general, DevOps methodologies ensure greater collaborations across development, engineering and operations teams. DataOps extends these capabilities and integrates platforms, analytics tools and automation to eliminate data silos across an organization. DataOps democratizes data use through self-service portals and creates infrastructure that makes information more accessible for both data scientists and business-side end users.

The right data science platform provides efficient tools for data migration and orchestration, whether vendor-driven or open source. Organizations can gear DataOps methodologies precisely to the languages and frameworks that in-house developers, engineers and operations teams regularly use. Adopting predefined tools and platforms via the cloud or open source also alleviates the expense and resource demands required to design and build customized data-centric infrastructures.

Within an organization, DataOps relies on automation across the entire IT infrastructure to offset manual IT operations tasks, such as quality assurance testing or CI/CD pipeline monitoring. Companies also gain general productivity improvements via the ability to use microservices and achieve higher degrees of self-sufficiency for IT and business teams.

Finally, DataOps counters the pervasiveness of data silos that disrupt most organizations and prevent them from gaining the potential benefits from the information they gather. For example, DataOps teams, as well as business end users, can deploy self-service platforms to use analytics tools and create their own data analyses and visualizations to achieve specific, targeted goals. Other methods could include tracking specific code variations via GitHub, or adopting Docker and Kubernetes to create container environments.

MLOps adoption

There are several stages to MLOps and facilitating the machine learning model lifecycle. These involve IT and business goal identification, data collection and annotation, model development and training, and final deployment and maintenance.

MLOps involves executing and monitoring data flows via multiple pipelines to properly train data models. It represents the next level in organizing data and model-based processes. MLOps entails tasks similar to those involved with extract, transform and load and master data management systems.

The key objectives of MLOps, which align with the goals of DataOps, are to streamline project deployments and improve data quality.

The key objectives of MLOps, which align with the goals of DataOps, are to streamline project deployments and improve data quality.

MLOps also fosters companywide collaboration and represents a relatively new AI concept, helping to bridge the divides among data experts, business professionals and operations. The goal is to work collaboratively using automation to deploy, monitor and govern machine learning projects within an organization. For example, data scientists need to test models using independent data, deploy challenger models and frequently recalibrate when combining AI with machine learning. Once data pipelines and machine learning models are successfully deployed, MLOps must undertake regular monitoring, as well as automate pipelines and data extraction wherever possible.

IT teams must create high-quality and efficient machine learning lifecycle management that conforms to industry standards and regulatory guidelines. Technicians must remain cognizant of the sensitive information they must handle with appropriate security practices.

A well-defined machine learning tracking system offers metrics that monitor the model's performance and how to isolate errors in production. MLOps teams can make functional comparisons and roll back to previous models when necessary. Via centralized metrics, KPIs and automated tracking, organizations can be confident that geographically dispersed models work in tandem and conform to production goals.

MLOps teams also use regular testing for drift detection, events and model changes. These steps all ensure that machine learning and AI deployments succeed past the experimental stage and into production.

The goals of DataOps vs. MLOps

When deciding on one approach versus another, it's useful to consider what they have in common: Both IT processes revolve around making data work better.

On one hand, DataOps is designed to manage and improve data flow at scale. Applying methodologies similar to DevOps, DataOps manages pipeline deployment and ensures that diverse information streams are usable and conform to specification. DataOps also relies on test and deployment automation to ensure fast CI/CD for those data pipelines.

On the other hand, MLOps is dedicated to ensuring that machine learning algorithms and AI systems are perfectly aligned and in sync. MLOps seamlessly integrates the amount -- and diversity -- of data to ensure that machine learning models perform as intended. A key goal of MLOps is to increase data science's effectiveness for insight-driven decisions within an organization.

As these two methodologies overlap and complement one another, it's useful to consider a new and emerging approach that might help simplify the choice for smaller companies and enterprises. Service orchestration and automation platforms offer an as-a-service approach to help IT operations teams orchestrate the automated processes that comprise end-to-end data pipelines.

This platform approach provides management and observability across an entire network of data pipelines. Organizations are then equipped to handle the coordination, scheduling and management of their data operations through a subscription service.